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kassidy-kollmann · Jul 5, 2024 · fde8725 · fde8725
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diff --git a/docs/cookbooks/result.rst b/docs/cookbooks/result.rst
@@ -1,7 +1,7 @@
-.. _database:
+.. _results:
 
-Database
-========
+Results
+=======
 
 A non-linear search fits a model to a dataset, returning a `Result` object that contains a lot of information on the
 model-fit. 

diff --git a/docs/cookbooks/samples.rst b/docs/cookbooks/samples.rst
@@ -1,7 +1,7 @@
-.. _result:
+.. _samples:
 
-Result
-======
+Samples
+=======
 
 After a non-linear search has completed, it returns a ``Result`` object that contains information on fit, such as
 the maximum likelihood model instance, the errors on each parameter and the Bayesian evidence.

diff --git a/docs/howtofit/howtofit.rst b/docs/howtofit/howtofit.rst
@@ -4,8 +4,8 @@ HowToFit Lectures
 =================
 
 To learn how to use **PyAutoFit**, the best starting point is the **HowToFit** lecture series, which are found on
-the ``autofit_workspace <https://github.com/Jammy2211/autofit_workspace>``_ and at
-our ``binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD>``_.
+the `autofit_workspace <https://github.com/Jammy2211/autofit_workspace>`_ and at
+our `binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD>`_.
 
 The lectures are provided as *Jupyter notebooks* and currently consist of 3 chapters: